Abstract
The high demand for generation and development in wind electrical power competitiveness has gained significant popularity in wind energy and speed forecasting models. It is also an essential method for planning the wind power plant system. Several models were created in the past to forest the speed and energy of the Wind. However, results have very low prediction accuracy due to their nonlinear and irregular characteristics. Therefore, a novel Modular Red Deer Neural System (MRDNS) was developed in this research to forecast wind speed and energy effectively. Primarily, the system accepted the data from the wind turbine SCADA database and preprocessed it to remove the training flaws. Further, the relevant features are extracted. The complexity of the prediction process was reduced by processing the relevant features. By analysing these features, the wind speed and energy were predicted in accordance with the fitness function of the MRDNS. The model obtained higher prediction accuracy. The recommended strategy was checked in the Python platform and the robustness metrics including RMSE, MSE, and precision were computed. The model scored 99.99% prediction accuracy; also gained 0.0017 MSE value, 0.0422 RMSE value for wind power forecasting and 0.0003 MSE, 0.0174 RMSE for wind speed forecasting.